Prevalence of Impairing Substance Use in Injured Drivers
Bibliographic record
Abstract
Importance: Impaired driving is an important public health issue, but its prevalence is challenging to monitor. Objectives: To report the prevalence of alcohol, cannabis, recreational drugs, and sedating medications in injured Canadian drivers, identify demographic and collision factors associated with drug or alcohol use, and compare the prevalence of drug-involved driving in different parts of Canada. Design, Setting, and Participants: This cross-sectional study prospectively obtained blood samples from injured drivers treated in 15 Canadian trauma centers and measured blood levels of tetrahydrocannabinol (THC; the main impairing compound in cannabis), alcohol, stimulants, opioids, and depressants from January 2019 to June 2023. Data were analyzed from April to May 2024. Exposure: Blood levels of THC, alcohol, stimulants, opioids, and depressants. Main Outcomes and Measures: Demographic and collision details were extracted from medical records. The crude prevalence for each substance class among all injured drivers and in selected subgroups was computed. Logistic regression models identified factors associated with substance use. Results: Of 8328 injured drivers (mean [SD] age, 43 [18] years; median [IQR] age, 40 [28-57] years; 5605 male [67.3%]; 2723 female [32.7%]), 4568 (54.9%) tested positive for an impairing substance and 1798 (21.6%) tested positive for 2 or more substance classes. Depressants, as a class, were detected in 2368 drivers (28.4%). THC was the most commonly detected single substance (1354 drivers [16.3%]), followed by alcohol (1341 drivers [16.1%]). Stimulants (1057 drivers [12.7%]) and opioids (905 drivers [10.9%]) were also detected. Substances were detected less often in drivers aged 75 years or older (195 of 455 drivers [42.9%]) and younger than 19 years (149 of 304 drivers [49.0%]). THC was most common in drivers aged 19 to 24 years, alcohol in drivers aged 19 to 34 years, stimulants in drivers aged 35 to 44 years, opioids in drivers aged 55 to 64 years, and depressants in drivers aged 65 to 74 years. Males had similar prevalence of substance use as females (3141 males [56.0%] vs 1427 females [52.4%]); more males used alcohol (adjusted odds ratio [aOR], 1.53; 95% CI, 1.21-1.92), cannabis (aOR, 1.66; 95% CI, 1.48-1.86), and stimulants (aOR, 1.53; 95% CI, 1.34-1.75), but males were less likely to have used a depressant (aOR, 0.54; 95% CI, 0.47-0.62). Rural drivers were more likely to use alcohol (aOR, 1.51; 95% CI, 1.29-1.76), stimulants (aOR, 1.32; 95 CI, 1.03-1.70), depressants (aOR, 1.28; 95% CI, 1.09-1.51), opioids (aOR, 1.26; 95% CI, 1.08-1.47), any substance (aOR, 1.40; 95% CI, 1.20-1.63), or multiple classes of substances (aOR, 1.55; 95 CI, 1.23-1.95). There was substantial geographic variation in the prevalence of substance use in injured drivers. Conclusions and Relevance: These findings suggest that impaired driving is a substantial road safety concern in Canada. Continued monitoring is required to develop targeted interventions and to evaluate the effectiveness of prevention measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".